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Bin Hu
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Jahr
Policy Optimization for Linear Control with Robustness Guarantee: Implicit Regularization and Global Convergence
K Zhang, B Hu, T Başar
SIAM Journal on Control and Optimization 59 (6), 4081-4109, 2021
143*2021
Dissipativity Theory for Nesterov's Accelerated Method
B Hu, L Lessard
International Conference on Machine Learning (ICML), 1549-1557, 2017
1222017
A robust accelerated optimization algorithm for strongly convex functions
S Cyrus, B Hu, B Van Scoy, L Lessard
2018 Annual American Control Conference (ACC), 1376-1381, 2018
772018
Characterizing the exact behaviors of temporal difference learning algorithms using Markov jump linear system theory
B Hu, U Syed
Advances in Neural Information Processing Systems, 8479-8490, 2019
632019
Analysis of biased stochastic gradient descent using sequential semidefinite programs
B Hu, P Seiler, L Lessard
Mathematical Programming 187, 383-408, 2020
532020
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems
K Zhang, B Hu, T Basar
Advances in Neural Information Processing Systems 33, 22056-22068, 2020
502020
Control interpretations for first-order optimization methods
B Hu, L Lessard
2017 American Control Conference (ACC), 3114-3119, 2017
452017
Toward a Theoretical Foundation of Policy Optimization for Learning Control Policies
B Hu, K Zhang, N Li, M Mesbahi, M Fazel, T Başar
Annual Review of Control, Robotics, and Autonomous Systems 6, 123-158, 2023
432023
Robust convergence analysis of distributed optimization algorithms
A Sundararajan, B Hu, L Lessard
2017 55th Annual Allerton Conference on Communication, Control, and …, 2017
422017
Exponential decay rate conditions for uncertain linear systems using integral quadratic constraints
B Hu, P Seiler
IEEE Transactions on Automatic Control 61 (11), 3631-3637, 2016
422016
Convergence guarantees of policy optimization methods for markovian jump linear systems
JP Jansch-Porto, B Hu, GE Dullerud
2020 American Control Conference (ACC), 2882-2887, 2020
372020
A unified analysis of stochastic optimization methods using jump system theory and quadratic constraints
B Hu, P Seiler, A Rantzer
Conference on Learning Theory (COLT), 1157-1189, 2017
342017
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity
K Zhang, X Zhang, B Hu, T Basar
Advances in Neural Information Processing Systems 34, 2949-2964, 2021
322021
Robustness analysis of uncertain discrete‐time systems with dissipation inequalities and integral quadratic constraints
B Hu, MJ Lacerda, P Seiler
International Journal of Robust and Nonlinear Control 27 (11), 1940-1962, 2017
272017
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
B Hu, S Wright, L Lessard
International Conference on Machine Learning (ICML), 2038-2047, 2018
262018
A Unified Algebraic Perspective on Lipschitz Neural Networks
A Araujo, AJ Havens, B Delattre, A Allauzen, B Hu
International Conference on Learning Representations, 2023
242023
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems
JP Jansch-Porto, B Hu, G Dullerud
Learning for Dynamics and Control, 947-957, 2020
202020
Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient Method and Global Convergence
JP Jansch-Porto, B Hu, GE Dullerud
IEEE Transactions on Automatic Control, 2022
17*2022
On Imitation Learning of Linear Control Policies: Enforcing Stability and Robustness Constraints via LMI Conditions
A Havens, B Hu
2021 American Control Conference (ACC), 882-887, 2021
172021
An Airborne Experimental Test Platform: Part 2
A LIE, H MOKHTARZADEH, P FREEMAN, J LARSON, T LAYH, BIN HU, ...
https://www.insidegnss.com/auto/mayjune14-LIE-Part2.pdf, 40-47, 2014
17*2014
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